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---
license: mit
base_model: Amna100/PreTraining-MLM
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fold_0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/amnasaeed100/FineTuning-ADE-change2/runs/zkyqf4w8)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/amnasaeed100/FineTuning-ADE-change2/runs/n6lnsbeg)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/amnasaeed100/FineTuning-ADE-change2/runs/k9jhon43)
# fold_0
This model is a fine-tuned version of [Amna100/PreTraining-MLM](https://huggingface.co/Amna100/PreTraining-MLM) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0105
- Precision: 0.7006
- Recall: 0.5978
- F1: 0.6452
- Accuracy: 0.9993
- Roc Auc: 0.9968
- Pr Auc: 0.9999
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Roc Auc | Pr Auc |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:------:|
| 0.0265 | 1.0 | 711 | 0.0162 | 0.4318 | 0.6196 | 0.5089 | 0.9987 | 0.9959 | 0.9998 |
| 0.0098 | 2.0 | 1422 | 0.0105 | 0.7006 | 0.5978 | 0.6452 | 0.9993 | 0.9968 | 0.9999 |
| 0.0038 | 3.0 | 2133 | 0.0123 | 0.6169 | 0.6739 | 0.6442 | 0.9992 | 0.9966 | 0.9999 |
| 0.0022 | 4.0 | 2844 | 0.0138 | 0.7006 | 0.6359 | 0.6667 | 0.9994 | 0.9963 | 0.9999 |
| 0.0004 | 5.0 | 3555 | 0.0151 | 0.7262 | 0.6630 | 0.6932 | 0.9994 | 0.9957 | 0.9999 |
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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